Deep Learning for Finance

Chapter 10. Deep Reinforcement Learning for Time Series Prediction

Reinforcement learning is a branch of machine learning that deals with sequential decision-making problems. Algorithms in this branch learn to make optimal decisions by interacting with an environment and receiving feedback in the form of rewards. In the context of time series forecasting, it can be used to develop models that make sequential predictions based on historical data. Traditional forecasting approaches often rely on statistical methods or supervised learning techniques, which assume independence between data points. However, time series data exhibits temporal dependencies and patterns, which may be effectively captured using reinforcement learning.

Reinforcement learning models for time series forecasting typically involve an agent that takes actions based on observed states and receives rewards based on the accuracy of its predictions. The agent learns through trial and error to maximize cumulative rewards over time. The key challenge is finding an optimal balance between exploration (trying out new actions) and exploitation (using learned knowledge).

This chapter gives a basic overview of reinforcement learning and deep reinforcement learning with regard to predicting time series data.

Intuition of Reinforcement Learning

Simplification is always the right path toward understanding more advanced details. So let’s look at reinforcement learning from a simple point of view before digging deeper.

Reinforcement ...